import tensorflow as tf
import tensorflow.keras.datasets as datasets
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# x: [60k, 28, 28]
# y: [60k]
(x, y), __ = datasets.mnist.load_data()

# x: [0~255] => [0~1.]
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255
y = tf.convert_to_tensor(y, dtype=tf.int32)

print(x.shape, y.shape, x.dtype, y.dtype)
print(tf.reduce_min(x), tf.reduce_max(x))
print(tf.reduce_min(y), tf.reduce_max(y))

# 将数据分成若干个batch，每个batch含128个数据
train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
train_iter = iter(train_db)
sample = next(train_iter)
print('batch:', sample[0].shape, sample[1].shape)

# 从截断的正态分布中输出随机值
# [b, 784] => [b, 256] => [b, 128] => [b, 10]
# [dim_in, dim_out], [dim_out]
# 必须转换为Variable类型才会进入GradientTape跟踪
w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
b1 = tf.Variable(tf.zeros([256]))
w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
b2 = tf.Variable(tf.zeros([128]))
w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))
lr = 1e-3

for epoch in range(10):
    for step, (x, y) in enumerate(train_db):
        # x:[128, 28, 28]
        # y:[128]

        # [b, 28, 28] -> [b, 28*28]
        x = tf.reshape(x, [-1, 28*28])

        with tf.GradientTape() as tape:
            # h1 = x@w1 + b1
            # [b,784]@[784,256]+[256] -> [b,256]+[256] -> [b,256]+[b,256]
            h1 = x@w1 + tf.broadcast_to(b1, [x.shape[0], 256])  # 也可以不使用broadcast，会自动进行转换
            h1 = tf.nn.relu(h1)  # 非线性化relu
            # [b,256]@[256,128]+[128] -> [b,128]+[128] -> [b,128]+[b,128]
            h2 = h1@w2 + tf.broadcast_to(b2, [x.shape[0], 128])
            h2 = tf.nn.relu(h2)
            # [b,128]@[128,10]+[10] -> [b,10]+[10] -> [b,10]+[b,10]
            out = h2@w3 + tf.broadcast_to(b3, [x.shape[0], 10])

            # 计算误差
            # out: [b, 10]
            # y: [b] -> [b,10]
            y_onehot = tf.one_hot(y, depth=10)

            # mse = mean(sum(y-out)^2)
            # [b, 10]
            loss = tf.square(y_onehot - out)
            # 转换为scalar
            loss = tf.reduce_mean(loss)

        # 计算梯度
        grads  = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
        # w1 = w1 - lr * w1_grad 等价于 w1.assign_sub(lr * grads[0])
        w1.assign_sub(lr * grads[0])
        b1.assign_sub(lr * grads[1])
        w2.assign_sub(lr * grads[2])
        b2.assign_sub(lr * grads[3])
        w3.assign_sub(lr * grads[4])
        b3.assign_sub(lr * grads[5])

        if step % 100 == 0:
            print(epoch, step, 'loss:', float(loss))